基于递归神经网络的神经机器翻译

Debajit Datta, Preetha Evangeline David, Dhruv Mittal, Anukriti Jain
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引用次数: 27

摘要

在这个全球化的时代,我们很可能会遇到与我们交流的语言不同的人或社区。为了解决由此引起的问题,我们正在开发机器翻译系统。谷歌有限责任公司等几家知名组织的开发人员一直在努力使用人工神经网络(ANN)等机器学习算法来支持机器翻译,以促进机器翻译。在这方面已经开发了一些神经机器翻译,但另一方面,递归神经网络(RNN)在这一领域的发展并不多。在我们的工作中,我们试图将RNN引入机器翻译领域,以承认RNN相对于ANN的优势。结果表明,RNN能够以适当的精度执行机器翻译。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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Neural Machine Translation using Recurrent Neural Network
In this era of globalization, it is quite likely to come across people or community who do not share the same language for communication as us. To acknowledge the problems caused by this, we have machine translation systems being developed. Developers of several reputed organizations like Google LLC, have been working to bring algorithms to support machine translations using machine learning algorithms like Artificial Neural Network (ANN) in order to facilitate machine translation. Several Neural Machine Translations have been developed in this regard, but Recurrent Neural Network (RNN), on the other hand, has not grown much in this field. In our work, we have tried to bring RNN in the field of machine translations, in order to acknowledge the benefits of RNN over ANN. The results show how RNN is able to perform machine translations with proper accuracy.
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